Landslide Hazard in Sukabumi Regency based on Weight of Evidence (WoE), Logistic Regresion (LR) and WoE-LR Combination Methods
Bahaya Longsor di Kabupaten Sukabumi berbasis Metode Weight of Evidence (WoE), Logistic Regression (LR) dan Kombinasi WoE-LR
The high frequency of landslides in Sukabumi Regency caused the need for data and information of potential landslides areas. The most widely used method to identify potential landslides is stastical method. Therefore, this study aims to predict landslide hazard in Sukabumi Regency. This research used Weight of Evidence (WoE), Logistic Regression (LR), and WoE-LR combination methods. Results showed that suitable parameters for running the models are distance from road, distance from river, distance from fault, SPI, TWI, elevation and slope. WoE method’s results showed elevation <300 m, distance from road >200 m and distance from friver >100 m are bad parameter classes to predict landslides in this study area. Whereas slope 8-15%, distance from road 31-70 m and elevation 700-800 m are good parameters to predict landslide potential. As for LR method, elevation and distance from road have significant effect on landslides. WoE-LR combination method’s results showed distance from road and SPI are bad parameters for predicting landslide potential. Conversely, slope and TWI are the best parameters to predict landslide hazards, including elevation, distance from fault and distance from river. Therefore it can be concluded that WoE-LR combination method is the best for predicting landslide hazard in the study area.
Atkinson, P. M. and Massari, R. 1998. Generalised Linear Modelling of Susceptibility to Landsliding in The Central Apennines, Italy. Computers & Geosciences vol. 24, 373-385.
Barbieri, G., dan Cambuli, P., 2009. “The Weight of Evidence Statistical Method in Landslide Susceptibility Mapping of the Rio Pardu Valley (Sardinia, Italy).” 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings, 2658–64.
Bonham-Carter, G. F. 1994. Geographic Information Systems for Geoscientists: Modelling with GIS. Computer Methods in the Geosciences, 267-302.
Dai, F.C., Lee, C.F. and Ngai, Y.Y 2002. Landslide Risk Assessment and Management: an Overview. Engineering Geology 64, 65-87.
Heckmann, T., Gegg, K., Gegg, A., dan Becht, M., 2014. Sample size matters: investigating the effect of sample size on a logistic regression susceptibility model for debris flows. Natural Hazards and Earth System Sciences, 14: 259-278.
Margarint, M. C., Grozavu, A., dan Patriche, C. V., 2013. Assessing the Spatial Variability of Weights of Landslide Causal Factors in Different Regions from Romania Using Logistic Regression. Natural Hazards and Earth System Sciences Discussions 1 (2): 1749–74.
Nefeslioglu, Hakan A., Gokceoglu, Candan., Sonmez, Harun., dan Gorum, Tolga., 2011. Medium-Scale Hazard Mapping for Shallow Landslide Initiation: The Buyukkoy Catchment Area (Cayeli, Rize, Turkey). Landslides 8 (4): 459–83.
Pamela, Sadisun, I.A., Kartiko, R.D., & Arifianti Y. 2018. Metode Kombinasi Weight of Evidance (W0E) dan Logistic Regresion (LR) untuk pemetaan Kerentanan Gerekan Tanah di Takengon Aceh. Jurnal Lingkungan dan Bencana Geologi, 77-86.
Pourghasemi, H. R., Moradi, H. R., dan Aghda, S. M. Fatemi., 2013. Landslide Susceptibility Mapping by Binary Logistic Regression, Analytical Hierarchy Process, and Statistical Index Models and Assessment of Their Performances.” Natural Hazards 69 (1): 749–79.
Samodra, G. 2016. Development of Risk Analysis Technique and Its Application To Geo-Disaster Management In Indonesia. Disertasi, Kyushu University Institutional Repository, Fukuoka. Jepang.
Zhou, S., Wang, W., Chen, G., Liu, B., & Fang, L. 2016. A combined weight of evidence and logistic regression method for susceptibility mapping of earthquake-induced landslides: A case study of the April 20, 2013 Lushan earthquake, China. Acta Geologica Sinica, Vol.90, 511-524.
Department of Soil Science and Land Resources Departemen Ilmu Tanah dan Sumberdaya Lahan, Faculty of Agriculture Fakultas Pertanian, IPB University